As today’s Finance leaders learn how to best navigate global uncertainty, increasingly more organizations are turning to extended planning & analysis (xP&A) to propel rolling forecasting and collaboration between Finance and Operations. At the forefront is a push toward a coordinated approach to artificial intelligence (AI)-enabled planning designed to achieve greater alignment between the strategic, financial, and operational levels within an organization. But the promise of transforming finance and operational planning into one cohesive ecosystem has fallen short due to fragmented solutions leaving little opportunity for true collaboration outside of Finance.
At least that’s what you’ve been led to believe.
Leveraging AI and other technologies have opened new possibilities for Finance leaders looking to drive continuous forecasting in the xP&A world. Accordingly, organizations of all sizes globally are leveraging AI to refine the rolling forecast process and increase business value. Many of the same leaders, however, are not putting the same emphasis on measuring and improving collaboration across the enterprise. Why? Well, fragmented solutions cause misaligned technology and forecasting processes, eroding organizational collaboration. And the level of effort to correct the imbalance can simply feel too steep.
Want proof? Just think about all the times Finance teams have had to chase monthly forecast files from sales, HR, supply chain, etc., only to find out the provided files are incomplete or have some anomalies that require follow-up.
Sounds familiar, doesn’t it? Whatever the case, it still feels like a waste of valuable time across all lines of business. Guarantee this kind of collaboration isn’t the kind for which organizations strive. But unfortunately, this world is the one in which most organizations live.
This environment is, however, one where AI can step in – especially amid the rise of xP&A – to reduce the level of effort needed to drive continuous collaboration with rolling forecasts. How? By automating siloed operating plans, speeding up the delivery of actionable insights, and aligning the Finance and Operations planning processes.
The forecasting process not only represents a core responsibility of the Finance function but also presents some of the most fundamental challenges as Finance must coordinate with other functions and departments in the organization. This core process requires Finance to leverage critical data points from Sales, Supply Chain, HR, and others across the organization and to gain the proper context from stakeholders. In essence, Finance must maintain full access to critical information and meaningful participation from partners in the business.
As Finance transforms into the central hub within an organization, siloed and fragmented solutions are not sufficient for sophisticated organizations that are streamlining their budgeting, forecasting, and planning (see Figure 1). After all, most organizations strive…
Collaboration is forever joined to rolling forecasts, both fostering flexibility and enabling a response to economic pressures that traditional forecasts can’t. Additionally, AI accelerates real-time data availability – allowing Finance, Operations, and Sales teams to regularly come together to assess the forecast makes for a more accurate and updated forecast.
We’ve previously defined a rolling forecast as “a management tool that enables organizations to continuously plan (i.e., forecast) over a set time horizon” vs. a calendar or fiscal year. For example, in a 12-month forecast period, as each month ends, another month will be added. In other words, forecasting involves always looking 12 months into the future (see Figure 2).
Best practice is to ensure rolling forecasts can extend (e.g., roll) beyond the current calendar or fiscal year-end. Most commonly, rolling forecasts contain a minimum of 12 forecast periods, but can also include 18, 24, or more periods depending on the needs and complexity of the organization.
In recent years, Finance leaders have taken on several major strategic initiatives to prepare for an ever-evolving landscape. Replacing detailed annual planning cycles – which take too long and result in a budget that’s out of date as soon as it’s complete. With continuous planning, however, Finance is more effective, focusing on rolling views that look 12 to 18 months ahead. How? Continuous plans enable managers to respond more rapidly to emerging events and trends and to changing business environments while increasing higher levels of corporate collaboration (see Figure 3).
Simply put, a rolling forecast is a call to action informing leadership of the need to engage and change when the forecast is updated. Why? Because rolling forecasts can transform the way Finance and Operations manage the business, obtaining better insights to make faster, more accurate decisions. And at the end of the day, leaders know that highly engaged, collaborative employees create a healthier overall organization that will increase performance.
Despite the rapid pace of adoption, many Finance leaders believe that FP&A teams must learn AI and machine learning (ML) modeling techniques when attempting to deploy AI-enabled rolling forecasts across the enterprise. Further, for organizations with existing AI investments, FP&A teams generally lack the dedicated business analysts and data science engineers required to build ML models. And as the adoption of AI and ML for rolling forecasts moves from fiction to fact, many FP&A teams are asking the same basic question: Where to begin?
To start, don’t let AI market noise derail the evaluation process. Here are 3 steps to consider in the process:
Most AI solutions offer everything from AI infrastructure solutions to data science toolkits and complete AI platforms to create and deploy ML models. While these are powerful tools addressing varying use cases, the tools aren’t designed for FP&A teams.
Conversely, purpose-built solutions like OneStream’s Sensible ML (see Figure 4) focus on a user-friendly, workflow-driven approach. That approach allows Finance and Operations users to build, deploy and consume time-series ML models directly within a unified experience while accelerating productivity for existing data science resources.
And unlike “most” predictive forecasting solutions – which look at prior results and statistics and generate forecasts based on what happened in the past – Sensible ML has the capability to not only look at prior results but also then take on additional business intuition (e.g., events, pricing, competitive information, and weather) to help drive more precise/robust forecasting.
Those capabilities are exactly why a unified technology platform is so critical. A unified platform instills confidence regarding information creation and gives organizations the flexibility to create operational relevance without compromising on control and governance.
AI-enhanced rolling forecasts can save huge amounts of work, freeing all managers and analysts to spend more time on value-added work. Adopting these forecasts will also improve the relationship between Finance and Business managers. After all, Finance will have more time to provide better service.
To learn more about key considerations for the journey towards xP&A, click here to download the White Paper titled “Unify Connecting Planning or Face the Hidden Costs.”